Dynamic Endpoint Profiling via Adaptive Probe Selection
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Solution Overview
Problem
Existing endpoint profiling techniques face challenges in accurately classifying endpoint devices due to ambiguous Media Access Control (MAC) addresses and the need for continuous, unnecessary data collection from multiple probe sources, especially with emerging IoT devices, leading to inefficiencies in processing and network resource usage.
Innovation Solution
A dynamic endpoint profiling system that uses a server and network device to selectively activate and deactivate probe functions based on the MAC address and extracted attributes, employing an inference engine for forward and backward chaining analysis to determine the most specific device type, thereby optimizing data collection and reducing unnecessary load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If continuous data collection from multiple probe sources is performed, then endpoint profiling accuracy is improved, but processing resources and network bandwidth consumption increase
Solution Approach 1:
The system dynamically adjusts probe activation based on classification confidence. When confidence exceeds a threshold, probing stops; when below threshold, additional probes are activated. This dynamic adaptation resolves the contradiction by avoiding continuous unnecessary probing while ensuring sufficient data collection for accurate classification.
Solution Approach 2:
The system uses feedback from probe results to determine whether additional probing is needed. The confidence threshold mechanism creates a feedback loop where classification results inform future probing decisions, optimizing the balance between accuracy and resource consumption.
2Measurement precision
If data collection from multiple probe sources is performed, then endpoint profiling accuracy is improved, but network bandwidth consumption increases
Solution Approach 1:
The system dynamically adjusts probe activation based on classification confidence. When confidence exceeds a threshold, probing stops; when below threshold, additional probes are activated. This dynamic adaptation resolves the contradiction by avoiding continuous unnecessary probing while ensuring sufficient data collection for accurate classification.
Solution Approach 2:
The system uses feedback from probe results to determine whether additional probing is needed. The confidence threshold mechanism creates a feedback loop where classification results inform future probing decisions, optimizing the balance between accuracy and resource consumption.
3Device complexity
If static probe configuration is used, then system simplicity is maintained, but adaptability to emerging devices and protocols decreases
Solution Approach 1:
The system transitions from static to dynamic probe configuration based on device characteristics and classification needs. The dynamic selection of probes tailored to specific device types maintains simplicity for known devices while providing adaptability for emerging devices through confidence-based probing.
Solution Approach 2:
The system changes probe configuration parameters dynamically based on device type, protocol, and classification confidence. This allows the system to adapt to emerging devices by adjusting probing parameters rather than requiring static reconfiguration.
Data Source
AI summary
A server is in communication with a network device that has network connectivity to an endpoint device. The server receives from the network device a packet that includes a Media Access Control (MAC) address of the endpoint device. A determination is made as to whether at least a portion of the MAC address matches stored information for MAC addresses of known endpoint devices. One or more attributes that carry further descriptive information of the endpoint device are extracted from the packet. It is determined based whether the endpoint device can be classified at a level of granularity according to a policy rule. If the endpoint device cannot be classified at the level of granularity, a probe function is dynamically selected based on the one or more attributes extracted from the packet and the MAC address to collect additional data about the endpoint device.